1. Field of Invention
This invention relates to determining user interests.
2. Description of Related Art
Conventional search systems allow users to find information by searching for explicitly entered search terms. Relevant documents are typically selected based on the occurrence frequency of the search terms. Term frequency and/or inverse document frequency techniques may be used to identify discriminative terms within a conventional information repository. The discriminative terms are used as indices. Documents are selected for the result set based on matches between the searched terms and indexed terms. As the information in an indexed repository increases, the terms in the index may no longer adequately discriminate between documents in the collection. That is, the large number of index terms result in large numbers of document matches. Kaplan et al. address these problems by allowing a user to pre-select terms and/or concepts of interest. These user-interest terms can then be coupled with a user's specific search terms to inform search engines and/or other information appliances with information about the specific interests of the user. The user-interest terms are then used to identify documents of likely interest to the user.
These conventional systems typically require the development of user-interest profiles to indicating user-interest information. More complicated user-interest profiles typically require skilled knowledge engineers and linguists to work closely with the user to explicitly specify user-interest information. The explicitly specified user-interest information is then transformed into appropriate user-interest terms and parameter weights. However, even when users are capable of directly specifying relevant terms and/or concepts of interest, they are frequently unable to assign the parameter weights necessary for an effective user-interest model. Moreover, conventional search systems do not facilitate dynamically updating the user-interest model as information retrieval patterns change.
Thus, systems and methods for selecting user-interest features and assigning the respective parameter weights of a user-interest model would be useful.